Mobile Botnet Detection via Artificial Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F17%3A63517333" target="_blank" >RIV/70883521:28140/17:63517333 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Mobile Botnet Detection via Artificial Neural Networks

Original language description

This paper deals with an area of cyber security and detection of the most dangerous category of mobile malware - botnets - via artificial neural networks. These specific pieces of mobile malware have a strange kind of behaviour. According to this pattern, botnets are not controlled by any expectable algorithm. On the contrary, humans managed their functioning via command and control servers (C&amp;C servers) or via peer-to-peer networks. Authors and their co-workers made an analysis of available current mobile botnets and they reveal some of their common features which were used later during training via artificial neural networks. The simulations were carried out with Levenberg-Marquardt training algorithm in the classical feed forward network. The results showed 100% successful training and almost 100% testing accuracy.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2017 International Conference on Logistics, Informatics and Service Sciences (LISS)

ISBN

978-1-5386-1047-3

ISSN

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e-ISSN

neuvedeno

Number of pages

5

Pages from-to

157-161

Publisher name

IEEE

Place of publication

New Jersey, Piscataway

Event location

Kyoto

Event date

Jul 24, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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